Probability and Statistics for Engineers
Instructor : Jeff Phillips (email) | Office hours: 2-3pm Tuesdays @ MEB 3442 (and often directly after class in WEB 1248)
TAs: Liang Zhang (email) | Office Hours: 10am - noon Mondays @ MEB 3423
         Shravanthi Manohar (email) | Office Hours: 12(noon)-1pm Wednesdays |and| 2:30-3:30pm Thursdays @ MEB 3423
Fall 2014 | Tuesdays, Thursdays 3:40 pm - 5:00 pm
WEB 2230
Catalog number: CS 3130 01 and ECE 3530 01

Class Syllabus
(old one)
An introduction to probability theory and statistics, with an emphasis on solving problems in computer science and engineering. Probability and statistics is an important foundation for computer science fields such as machine learning, artificial intelligence, computer graphics, randomized algorithms, image processing, and scientific simulations. Topics in probability include discrete and continuous random variables, probability distributions, sums and functions of random variables, the law of large numbers, and the central limit theorem. Topics in statistics include sample mean and variance, estimating distributions, correlation, regression, and hypothesis testing. Beyond the fundamentals, this course will also focus on modern computational methods such as simulation and the bootstrap. Students will learn statistical computing using the freely available R statistics software.

Book: A Modern Introduction to Probability and Statistics: Understanding Why and How by Dekking, Kraaikamp, Lopuhaa, and Meester.
An electronic version of this book is freely available through the University: here. To access the book you must be visiting this website from the campus network. Or if you are off campus, you can access it using VPN.

Homeworks can be turned in as a hard copy at the start of the class in which they are due. Or they can be turned in through Canvas as a pdf by the start of class. If you choose to create a pdf, I strongly suggest using Latex. Some helpful materials can be found here: latex.

Schedule: (subject to change - linked material from old version of class marked as (2013), may be slightly updated)
Date Topic Link Assignment (latex)
Tu 8.26 Introduction Ch 1
Th 8.28 Sample Spaces, Events, Probability Ch 2 HW 1 out
Tu 9.2 Conditional Probability Ch 3
Th 9.4 Total Probability Ch 3
Tu 9.9 Independence Ch 3
Th 9.11 Bayes Rule Ch 3 HW 1 due, HW 2 out
Tu 9.16 Discrete Random Variables Ch 4
Th 9.18 Binomial and Geometric Distributions Ch 4 Quiz 1
Tu 9.23 R for Discrete Distributions Ch 5 HW 2 due, HW 3 out
Th 9.25 Continuous Random Variables Ch 5
Tu 9.30 Gaussian Random Variables Ch 5
Th 10.2 (slack)
Tu 10.7 Expectation Ch 7 HW 3 due, HW 4 out
Th 10.9 Variance Ch 7 Quiz 2
Tu 10.14 (Fall Break - No Class)
Th 10.16 (Fall Break - No Class)
Tu 10.21 review + Joint Probability for Discrete RVs Ch 9
Th 10.23 Joint Probability for Discrete and Continuous RVs Ch 9
Tu 10.28 Joint Probability for Continuous RVs Ch 9 HW 5 out
Th 10.30 Covariance and Correlation Ch 10 HW 4 due
Tu 11.4 Bivariate Gaussian + Gaussian and Bivariate in R Ch 10
Th 11.6 Intro to Statistics + R examples Ch 15-17 Quiz 3
Tu 11.11 Estimation and Bias Ch 19 HW 5 due, HW 6 out
Th 11.13 Confidence Intervals Ch 23
Tu 11.18 Confidence Intervals Ch 23
Th 11.20 more on Confidence Intervals Ch 6 HW 6 due, HW 7 out
Tu 11.25 Hypothesis Testing Ch 25-26 Quiz 4
Th 11.27 (Thanksgiving - No Class)
Tu 12.2 Hypothesis Testing (+ R code + pic) Ch 25-27 HW 8 out
Th 12.4 Covariance Hypothesis Testing (+ R code) Ch 28 HW 7 due
Tu 12.9 Review
Th 12.11 Linear Regression (+ R code + data) Ch 17.4 + Ch 22 Quiz 5
Fr 12.12 HW 8 due
Tu 12.16 FINAL EXAM (practice exam + solutions) 3:30 - 5:30